Hugging Face Daily PapersHongyao Tang, Yi Ma, Pengyi LiSep 11, 20261 min readpaperadvanced

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

Summary

The paper introduces Generalized Agent Iteration (GAI), a formal framework that unifies classical iterative policy improvement (GPI) and recursive self‑improvement (RSI). GAI treats an agent as a set of modifiable components and models learning as a loop of evaluation and improvement. Two binary “dials”—whether the improvement mechanism is internal to the agent and whether the evaluation standard…

GAI abstracts both GPI and RSI into a single iterative process, highlighting the role of component boundaries and evaluation criteria.
The two‑dial taxonomy (internal vs. external improvement, external vs. internal evaluation) yields four distinct system classes, clarifying where current AI approaches sit.
By formalizing RSI, the framework makes it possible to state the failure modes of self‑improvement as explicit conditions, aiding rigorous analysis.
The paper is primarily conceptual; it does not provide empirical experiments or concrete algorithmic instantiations of GAI.

A unified formalism for self‑improving agents could become a reference point for the AI safety and alignment community, enabling systematic comparison of proposals that claim recursive improvement. It also bridges a gap between reinforcement‑learning theory (GPI) and speculative AI architectures, p…

6/10

machine learning reinforcement learning artificial intelligence theory recursive self improvement

Related reading

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement arxiv.org
The paper defines recursive self‑improvement (RSI) for AI, introduces the Headroom‑Closed Index to expose current LLM limits, and proposes a staged roadmap toward full RSI. It surveys application scenarios and outlines practical challenges.
Hugging Face Daily Papersarxiv.orgYi Duan, Ying Liu, Zirui Tang12d1 minpaperHN3
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses arxiv.org
RRSI adds budget‑annealed edit limits, trajectory exploration incentives, a benchmark‑aware critic, and a pruning step to recursive self‑improvement of LLM agent harnesses, yielding up to +14.1 points on in‑distribution tasks and +4.7 on OOD benchmarks while cutting policy token usage by 30%.
Hugging Face Daily Papersarxiv.orgPeng Xia, Rujun Han, Zifeng Wang1d1 minpaper
ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement arxiv.org
ModularRSI proposes a contrastive, modular approach to evolve agent harnesses by independently updating five functional modules and aggregating evidence across 2,000 benchmark‑disjoint tasks. Experiments on TB2.0 and SWE‑Bench Verified show consistent gains on unseen tasks and cross‑model transfer.
Hugging Face Daily Papersarxiv.orgSiwei Wu, Jincheng Ren, Yizhi Li8d1 minpaper
RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning arxiv.org
RetireOPD introduces a self‑retiring on‑policy distillation framework for multi‑turn RL agents. A skill‑conditioned teacher is first trained with environment rewards, then a skill‑free student learns jointly via RL and token‑level distillation. The student automatically drops the teacher once its performance gap stops shrinking and it reaches a target success‑rate fraction, after which training c…
Hugging Face Daily Papersarxiv.orgYan Yu, Zhengxi Lu, Yizhou Liu5d1 minpaper
One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents arxiv.org
The authors propose a category‑aware expert‑training loop (RRE) and a multi‑teacher on‑policy distillation (MOPD) to improve software‑engineering agents across heterogeneous task categories. On Pro‑618 and SWE‑bench Multilingual the final student model reaches 58.04% and 59.00% resolution, 5.39 and 2.78 points above the base.
Hugging Face Daily Papersarxiv.orgJie Zhao, Ziyu Jiang, Suhang Zheng2d1 minpaper
In-Context Robot Learning with VLM Agents arxiv.org
GPT‑Policy is a framework that lets a large vision‑language model (e.g. GPT‑6 Astra) perform in‑context robot learning: a context compiler extracts visual transitions from demos, the VLM proposes tool actions, and a constrained controller verifies and executes them. Real‑robot experiments show that raw video demos improve success rates even without explicit action labels, and that providing align…
Hugging Face Daily Papersarxiv.orgDongzhou Cheng, Taoran Yi, Ye Fang6d1 minpaper

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

Summary

Related reading

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvementarxiv.org

RRSI: Regularized Recursive Self-Improvement of Agent Harnessesarxiv.org

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvementarxiv.org

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learningarxiv.org

One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agentsarxiv.org

In-Context Robot Learning with VLM Agentsarxiv.org

The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement arxiv.org

RRSI: Regularized Recursive Self-Improvement of Agent Harnesses arxiv.org

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement arxiv.org

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning arxiv.org

One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents arxiv.org

In-Context Robot Learning with VLM Agents arxiv.org